Scatter plot with plotly express¶

Plotly Express functions take as a first argument a tidy pandas.DataFrame. With px.scatter, each data point is represented as a marker point, which location is given by the x and y columns.

In [1]:

import plotly.express as px
iris = px.data.iris()
fig = px.scatter(iris, x="sepal_width", y="sepal_length")
fig.show()

Set size and color with column names¶

Note that color and size data are added to hover information. You can add other columns to hover data with the hover_data argument of px.scatter.

In [2]:

import plotly.express as px
iris = px.data.iris()
fig = px.scatter(iris, x="sepal_width", y="sepal_length", color="species",
                 size='petal_length', hover_data=['petal_width'])
fig.show()

Line plot with plotly express¶

In [3]:

import plotly.express as px
gapminder = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(gapminder, x='year', y='lifeExp', color='country')
fig.show()

Scatter and line plot with go.Scatter¶

When data are not available as tidy dataframes, it is possible to use the more generic go.Scatter function from plotly.graph_objects. Whereas plotly.express has two functions scatter and line, go.Scatter can be used both for plotting points (makers) or lines, depending on the value of mode. The different options of go.Scatter are documented in its reference page.

Simple Scatter Plot¶

In [4]:

import plotly.graph_objects as go
import numpy as np

N = 1000
t = np.linspace(0, 10, 100)
y = np.sin(t)

fig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers'))

fig.show()

Line and Scatter Plots¶

Use mode argument to choose between markers, lines, or a combination of both. For more options about line plots, see also the line charts notebook and the filled area plots notebook.

In [5]:

import plotly.graph_objects as go

# Create random data with numpy
import numpy as np
np.random.seed(1)

N = 100
random_x = np.linspace(0, 1, N)
random_y0 = np.random.randn(N) + 5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N) - 5

fig = go.Figure()

# Add traces
fig.add_trace(go.Scatter(x=random_x, y=random_y0,
                    mode='markers',
                    name='markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
                    mode='lines+markers',
                    name='lines+markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
                    mode='lines',
                    name='lines'))

fig.show()

Bubble Scatter Plots¶

In bubble charts, a third dimension of the data is shown through the size of markers. For more examples, see the bubble chart notebook

In [6]:

import plotly.graph_objects as go

fig = go.Figure(data=go.Scatter(
    x=[1, 2, 3, 4],
    y=[10, 11, 12, 13],
    mode='markers',
    marker=dict(size=[40, 60, 80, 100],
                color=[0, 1, 2, 3])
))

fig.show()

Style Scatter Plots¶

In [7]:

import plotly.graph_objects as go
import numpy as np


t = np.linspace(0, 10, 100)

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=t, y=np.sin(t),
    name='sin',
    mode='markers',
    marker_color='rgba(152, 0, 0, .8)'
))

fig.add_trace(go.Scatter(
    x=t, y=np.cos(t),
    name='cos',
    marker_color='rgba(255, 182, 193, .9)'
))

# Set options common to all traces with fig.update_traces
fig.update_traces(mode='markers', marker_line_width=2, marker_size=10)
fig.update_layout(title='Styled Scatter',
                  yaxis_zeroline=False, xaxis_zeroline=False)


fig.show()

Data Labels on Hover¶

In [8]:

import plotly.graph_objects as go
import pandas as pd

data= pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv")

fig = go.Figure(data=go.Scatter(x=data['Postal'],
                                y=data['Population'],
                                mode='markers',
                                marker_color=data['Population'],
                                text=data['State'])) # hover text goes here

fig.update_layout(title='Population of USA States')
fig.show()

Scatter with a Color Dimension¶

In [9]:

import plotly.graph_objects as go
import numpy as np

fig = go.Figure(data=go.Scatter(
    y = np.random.randn(500),
    mode='markers',
    marker=dict(
        size=16,
        color=np.random.randn(500), #set color equal to a variable
        colorscale='Viridis', # one of plotly colorscales
        showscale=True
    )
))

fig.show()

Large Data Sets¶

Now in Ploty you can implement WebGL with Scattergl() in place of Scatter()
for increased speed, improved interactivity, and the ability to plot even more data!

In [10]:

import plotly.graph_objects as go
import numpy as np

N = 100000
fig = go.Figure(data=go.Scattergl(
    x = np.random.randn(N),
    y = np.random.randn(N),
    mode='markers',
    marker=dict(
        color=np.random.randn(N),
        colorscale='Viridis',
        line_width=1
    )
))

fig.show()

In [11]:

import plotly.graph_objects as go
import numpy as np

N = 100000
r = np.random.uniform(0, 1, N)
theta = np.random.uniform(0, 2*np.pi, N)

fig = go.Figure(data=go.Scattergl(
    x = r * np.cos(theta), # non-uniform distribution
    y = r * np.sin(theta), # zoom to see more points at the center
    mode='markers',
    marker=dict(
        color=np.random.randn(N),
        colorscale='Viridis',
        line_width=1
    )
))

fig.show()

Dash Example¶

Dash is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its source code can easily be deployed to a PaaS.

In [12]:

from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-linescatterplot/", width="100%",height="750px", frameBorder="0")

Out[12]:

In [13]:

from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-linescatterplot/code", width="100%",height=500, frameBorder="0")

Out[13]:

Reference¶

See https://plot.ly/python/reference/#scatter or https://plot.ly/python/reference/#scattergl for more information and chart attribute options!